Patient networks! in cancer:! a platform for data integration
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1 Anna Goldenberg and The Goldenberg Lab Patient networks! in cancer:! a platform for data integration
2 Outline o Data integra-on problem setup o Pa-ent network representa-on why and how o Similarity Network Fusion novel integra-on method o Network driven analysis: o Cancer heterogeneity o Differen-al feature selec-on o Missing data o Random entries o Pa-ents o Taking networks further: o Survival analysis (novel formula-on) o Personalized medicine
3 Problem setup
4 Problem setup
5 Problem setup
6 Problem setup Large number of measurements, small sample sizes (p>>n) Need to integrate common and complementary informa-on Not all measurements can be mapped to the same unit (gene)
7 Goldenberg Lab: Similarity Network Fusion
8 Goldenberg Lab: Similarity Network Fusion
9 Goldenberg Lab: Similarity Network Fusion Step 1. Construct a similarity network for each data source"
10 Goldenberg Lab: Similarity Network Fusion Step 1. Construct a similarity network for each data source" Step 2. Integrate networks using nonlinear fusion method"
11 1. Construct similarity networks mrna expression genes Patients
12 1. Construct similarity networks Pa-ent similarity: W (i, j) =exp( (x i,x j ) 2 2 ij ) Adjacency matrix: P (i, j) = Pk2V W (i, j) W (i, k), mrna expression genes Patients Patients Patients
13 1. Construct similarity networks Sparsifica-on s: W (i, j) ifxj 2 KNN(x W(i, j) = i ) 0 otherwise 1) 2) P(i, j) = P x k 2KNN(x i ) W(i, j) W(i, k) mrna expression genes Patients p1 p2 Patients Patients p9 p8
14 1. Construct similarity networks mrna expression genes p1 p2 Patients p9 p8 DNA methylation CpGs Patients
15 1. Construct similarity networks mrna expression genes p1 p2 Patients p9 DNA methylation CpGs p1 p2 p8 Patients p9 p8
16 2. Combine networks Sample Similarity Networks Patient Patient similarity: mrna-based DNA Methylation-based Supported by all data
17 2. Combine networks Sample Similarity Networks Fusion P (1) t+1 = P(1) (P (2) t ) (P (1) ) 0 P (2) t+1 = P(2) (P (1) t ) (P (2) ) 0 Patient Patient similarity: mrna-based DNA Methylation-based Supported by all data
18 2. Combine networks Sample Similarity Networks Fusion Itera-ons P (1) t+1 = P(1) (P (2) t ) (P (1) ) 0 P (2) t+1 = P(2) (P (1) t ) (P (2) ) 0 Patient Patient similarity: mrna-based DNA Methylation-based Supported by all data
19 2. Combine networks Sample Similarity Networks Fusion Itera-ons Fused Similarity Network nd empiric kw t+1 W t k kw t k 10-6 Patient Patient similarity: mrna-based DNA Methylation-based Supported by all data
20 Network Fusion Fusing 2 networks: P (1) t+1 = P(1) (P (2) t ) (P (1) ) 0 P (2) t+1 = P(2) (P (1) t ) (P (2) ) 0 Fusing m networks: t+1 = P (i) 1 ( m 1 P (i) X j6=i P (j) t ) (P (i) ) 0 + I
21 Experiments Data:! " "5 TCGA cancers" "METABRIC (Large " Breast Cancer db)" Comparative Methods:! " "Concatenation" "icluster" "PDSB" "Multiple kernel learning" Criteria:!! " " -log 10 (log rank pvalue)" " Silhouette score (cluster homogeneity)" " Running time"
22 Simula-on 1 complementarity
23 Simula-on 1 convergence 20% of pa-ents are mislabeled
24 Simula-on 2 - removing noise
25 Simula-on 2 - removing noise
26 TCGA Data
27 Case study: Glioblastoma DNA methyla-on data mrna expression data mrna expression mirna expression Bo Wang
28 Case study: Glioblastoma DNA methyla-on data FUSED mrna expression data mrna expression mirna expression Bo Wang
29 Clinical proper-es of the subtypes Survival probability Survival Probability p- value = 2x10-4 Subtype1 Subtype2 Subtype3 Subtype 1 Subtype 2 Subtype Survival Time/month Survival time (months)
30 Clinical proper-es of the subtypes Survival probability Survival Probability p- value = 2x10-4 Subtype1 Subtype2 Subtype3 Subtype 1 Subtype 2 Subtype Survival Time/month Survival time (months) Age (years) 60 Age Subtype 1 Subtype 2 Subtype 3 Subtype1 Subtype2 Subtype3 p- value = 3x10-5
31 Clinical proper-es of the subtypes Survival probability Age (years) Survival Probability Age Survival Time/month p- value = 2x10-4 Subtype1 Subtype2 Subtype3 Subtype 1 Subtype 2 Subtype 3 Survival time (months) Subtype 1 Subtype 2 Subtype 3 Survival Probability Treated Untreated Survival 40 Time / month Subtype1 Subtype2 Subtype3 Survival time (months) p- value = 3x10-5 p = Survival probability Subtype 1
32 Biological characteriza-on of the subtypes Subtype 1 Subtype 2 Subtype 3 Chr.07 gain Chr.19 gain Chr.05 loss Chr.10 loss Chr.21 loss EGFR ampl. CNKN2A del. PTEN del. PIK3R1 del. RB1 ampl. CDK6 ampl. IDH1 EGFR PTEN 1 NA 0
33 Feature Selec-on Standard t- test Differen.al analysis Subtype 1 Subtype 2 Subtype mrna Expression DNA Methylation mirna Expression Bo Wang
34 Feature Selec-on Standard t- test Differen.al analysis Subtype 1 Subtype 2 Network- based NMI Differen.al analysis Subtype mrna Expression DNA Methylation mirna Expression Bo Wang
35 Gene Pre- selec-on in GBM -log 10 (p-value) log(p) Number of Genes Number of genes x10 4 PNF Concatenation icluster p-value=0.05 Genes are ordered by significance of the differen-al values between tumor and normal Bo Wang
36 Gene Pre- selec-on in GBM -log 10 (p-value) log(p) Number of Genes Number of genes x10 4 Silhouette Silhouette Fusion+Spectral Concatenation icluster Number of Genes Number of genes x10 4 PNF Concatenation icluster p-value=0.05 Genes are ordered by significance of the differen-al values between tumor and normal Bo Wang
37 Gene Pre- selec-on in GBM -log 10 (p-value) log(p) Number of Genes Number of genes x10 4 Silhouette Silhouette Fusion+Spectral Concatenation icluster Number of Genes Number of genes x10 4 Running time (min) Running Time(min) Fusion+Spectral Concatenation icluster Number of Genes Number of genes x10 4 PNF Concatenation icluster p-value=0.05 Genes are ordered by significance of the differen-al values between tumor and normal Bo Wang
38 Gene pre- selec-on across cancers Bo Wang
39 Clustering of the network Bo Wang
40 Pa-ent networks framework advantages ü Creates a unified view of pa-ents based on mul-ple heterogeneous sources ü Integrates gene and non- gene based data ü No need to do gene pre- selec-on ü Robust to different types of noise ü Scalable
41 Pa-ent networks ü Obtain superior results on regular tasks such as subtyping ü No feature pre- selec-on ü Imputa-on is a lot faster Transforma-ve power ü Can easily impute missing pa-ents, without of pa-ent networks impu-ng original data
42 Breast Cancer (METABRIC example) CNV and expression data Discovery: 997 pa-ents Valida-on: 995 pa-ents Nature, 2012 established
43 Breast Cancer (METABRIC example) CNV and expression data Discovery: 997 pa-ents Valida-on: 995 pa-ents Nature, 2012 established So how many subtypes are there really in breast cancer?
44 Predic-ng using the network
45 Predic-ng using the network lp z z z i i n () log exp 1 X X i T j T j R t i ( ) Cox objec-ve
46 Predic-ng using the network Cox objec-ve n lp() z T i z log T Xi exp X j z i 1 j R( t i ) Our network- regularized objec-ve n lp() z i Xi T z X T log exp( j z) i 1 j R( ti ) i j ( Xi T z X T j z) 2 w ij where is the regularizing coefficient. Newton optim
47 Predic-ng using the network Breast Cancer (METABRIC example) CNV and expression data Discovery: 997 pa-ents Valida-on: 995 pa-ents Nature, 2012 established
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